Agent Conversation: gemini-3.1-pro-preview
Model: gemini-3.1-pro-preview
Total steps: 7
Messages: 18
UserStep 0
Task: llm-pretrain-loss
LLM Pretraining: Loss Function Optimization
Research Question
Design an improved loss function for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to standard cross-entropy.
What You Can Modify
The compute_loss function (lines 189-191) in custom_pretrain.py:
- Loss function formulation (default: standard cross-entropy)
- Logit processing (e.g., softcapping, temperature scaling)
- Regularization terms (e.g., z-loss, entropy penalties)
- Label distribution modifications (e.g., label smoothing)
Note: The function signature compute_loss(logits, targets) must be preserved. logits has shape (B, T, V) and targets has shape (B, T). The function is called inside the model's forward pass during training.
Evaluation
- Metric: Validation loss (cross-entropy, lower is better)
- Model sizes: GPT-2 124M (12L/12H/768D) and GPT-2 1.5B (48L/25H/1600D, 4-GPU DDP)
- Dataset: FineWeb 10B (GPT-2 tokenizer)
- Training: 5000 iterations, batch_size=12, block_size=1024, grad_accum=5
nanoGPT/custom_pretrain.py [EDITABLE — lines 188–191, lines 247–249 only]
1: """Custom GPT-2 Pretraining Script
2: Based on Andrej Karpathy's nanoGPT, evaluated on FineWeb dataset.
3: """
4:
5: import math
6: import inspect
7: import os
8: import time
9: from contextlib import nullcontext
10: from dataclasses import dataclass
11:
12: import numpy as np
13: import torch
14: import torch.nn as nn
15: from torch.nn import functional as F
16:
17: # ============================================================================
18: # Model Components
19: # ============================================================================
20:
21: # ── Normalization ──────────────────────────────────────────────────────────
22: class LayerNorm(nn.Module):
23: """LayerNorm but with an optional bias."""
24: def __init__(self, ndim, bias):
25: super().__init__()
26: self.weight = nn.Parameter(torch.ones(ndim))
27: self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
28:
29: def forward(self, input):
30: return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
31:
32: # ── Self-Attention ─────────────────────────────────────────────────────────
33: class CausalSelfAttention(nn.Module):
34: def __init__(self, config):
35: super().__init__()
36: assert config.n_embd % config.n_head == 0
37: self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
38: self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
39: self.attn_dropout = nn.Dropout(config.dropout)
40: self.resid_dropout = nn.Dropout(config.dropout)
41: self.n_head = config.n_head
AssistantStep 1edit
Tool Input
1{
2 "end_line": 191,
3 "op": "replace",
4 "content": "def compute_loss(logits, targets):\n \"\"\"\n Self-Distilled Label Smoothing (SDLS) + Softcapping + Z-loss.\n Motivation: \n - SDLS blends hard targets with model's softened beliefs (tau>1) for semantic-aware smoothing, \n avoiding the destruction of semantic correlations caused by uniform label smoothing.\n - Gemma-style softcapping restricts maximum logit bounds to prevent instability.\n - Z-loss prevents log-partition drift.\n \"\"\"\n cap_val = 30.0\n capped = cap_val * torch.tanh(logits.float() / cap_val)\n \n flat_logits = capped.view(-1, capped.size(-1))\n flat_targets = targets.view(-1)\n \n if not torch.is_grad_enabled():\n return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1)\n \n valid_mask = flat_targets != -1\n valid_logits = flat_logits[valid_mask]\n valid_targets = flat_targets[valid_mask]\n \n # SDLS hyperparameters\n alpha = 0.95 # 95% hard target, 5% soft distillation (comparable to eps=0.05 uniform smoothing)\n tau = 2.0 # Temperature. tau > 1 flattens the distribution for valid smoothing gradients.\n \n with torch.no_grad():\n self_preds = F.softmax(valid_logits / tau, dim=-1)\n \n log_probs = F.log_softmax(valid_logits, dim=-1)\n hard_loss = F.nll_loss(log_probs, valid_targets)\n soft_loss = -torch.sum(self_preds * log_probs, dim=-1).mean()\n \n # Z-Loss Regularization\n log_z = torch.logsumexp(valid_logits, dim=-1)\n z_loss = (log_z ** 2).mean()\n \n return alpha * hard_loss + (1.0 - alpha) * soft_loss + 1e-4 * z_loss",
5 "filename": "nanoGPT/custom_pretrain.py",
6 "start_line": 188
7}Tool ResultStep 1
OK: Replaced lines 188..191 with 38 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–225, 281–283. [Current file: nanoGPT/custom_pretrain.py | editable: 188–225 | total: 473 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """ 190 Self-Distilled Label Smoothing (SDLS) + Softcapping + Z-loss. 191 Motivation: 192 - SDLS blends hard targets with model's softened beliefs (tau>1) for semantic-aware smoothing, 193 avoiding the destruction of semantic correlations caused by uniform label smoothing. 194 - Gemma-style softcapping restricts maximum logit bounds to prevent instability. 195 - Z-loss prevents log-partition drift. 196 """ 197 cap_val = 30.0 198 capped = cap_val * torch.tanh(logits.float() / cap_val) 199 200 flat_logits = capped.view(-1, cappe
AssistantStep 2test
Tool ResultStep 2
[Test #1] (2 tests remaining; call submit(n=1) to submit this result)
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=9000, train_loss=2.6724, val_loss=2.3971
TRAIN_METRICS: step=10000, train_loss=2.6506, val_loss=2.3694
TRAIN_METRICS: step=11000, train_loss=2.6324, val_loss=2.3595
TRAIN_METRICS: step=12000, train_loss=2.6139, val_loss=2.3430
TRAIN_METRICS: step=13000, train_loss=2.5980, val_loss=2.3321
Final metrics (gpt-345m): val_loss_gpt-345m=2.3264, wikitext2_ppl_gpt-345m=46.4100, lambada_ppl_gpt-345m=70.7500
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=54.3400, hellaswag_lm-eval-345m=33.6700, piqa_lm-eval-345m=63.7600, winogrande_lm-eval-345m=49.2500
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3264, 'wikitext2_ppl_gpt-345m': 46.41, 'lambada_ppl_gpt-345m': 70.75, 'elapsed_gpt-345m': 23229, 'arc_easy_lm-eval-345m': 54.34, 'hellaswag_lm-eval-345m': 33.67, 'piqa_lm-eval-345m': 63.76, '
AssistantStep 3edit
Tool Input
1{
2 "content": "def compute_loss(logits, targets):\n \"\"\"\n Modded-NanoGPT Softcapping + Standard Label Smoothing + Poly-1 Loss + Z-loss.\n Motivation:\n - Modded-NanoGPT softcap is highly tuned (A=23, B=5, C=7.5) for the GPT-2 vocabulary size.\n - Poly-1 Loss explicitly manipulates the polynomial expansion of cross entropy to balance gradient signal.\n \"\"\"\n flat_logits = logits.float().view(-1, logits.size(-1))\n flat_targets = targets.view(-1)\n \n # Modded-NanoGPT softcap\n A, B, C = 23.0, 5.0, 7.5\n capped = A * torch.sigmoid((flat_logits + B) / C)\n \n if not torch.is_grad_enabled():\n return F.cross_entropy(capped, flat_targets, ignore_index=-1)\n \n valid_mask = flat_targets != -1\n valid_logits = capped[valid_mask]\n valid_targets = flat_targets[valid_mask]\n \n with torch.no_grad():\n pure_ce = F.cross_entropy(valid_logits, valid_targets, reduction='none')\n pt = torch.exp(-pure_ce)\n \n smoothed_ce = F.cross_entropy(valid_logits, valid_targets, reduction='none', label_smoothing=0.05)\n \n # Poly-1 penalty upweights hard/easy examples based on sign. Let's strictly use +0.5.\n alpha = 0.5\n poly_loss = (smoothed_ce + alpha * (1 - pt)).mean()\n \n # Z-Loss Regularization\n log_z = torch.logsumexp(valid_logits, dim=-1)\n z_loss = (log_z ** 2).mean()\n \n return poly_loss + 1e-4 * z_loss",
3 "op": "replace",
4 "filename": "nanoGPT/custom_pretrain.py",
5 "start_line": 188,
6 "end_line": 225
7}Tool ResultStep 3
OK: Replaced lines 188..225 with 36 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–223, 279–281. [Current file: nanoGPT/custom_pretrain.py | editable: 188–223 | total: 471 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """ 190 Modded-NanoGPT Softcapping + Standard Label Smoothing + Poly-1 Loss + Z-loss. 191 Motivation: 192 - Modded-NanoGPT softcap is highly tuned (A=23, B=5, C=7.5) for the GPT-2 vocabulary size. 193 - Poly-1 Loss explicitly manipulates the polynomial expansion of cross entropy to balance gradient signal. 194 """ 195 flat_logits = logits.float().view(-1, logits.size(-1)) 196 flat_targets = targets.view(-1) 197 198 # Modded-NanoGPT softcap 199 A, B, C = 23.0, 5.0, 7.5 200 capped = A * torch.sigmoid((flat_logits + B) / C) 201
AssistantStep 4test
AssistantStep 4test
Tool ResultStep 4
[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final)
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=0, train_loss=10.9222, val_loss=10.9217
TRAIN_METRICS: step=1000, train_loss=3.7021, val_loss=3.4925
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=54.3400, hellaswag_lm-eval-345m=33.6700, piqa_lm-eval-345m=63.7600, winogrande_lm-eval-345m=49.2500
[Leaderboard] Results saved: {'arc_easy_lm-eval-345m': 54.34, 'hellaswag_lm-eval-345m': 33.67, 'piqa_lm-eval-345m': 63.76, 'winogrande_lm-eval-345m': 49.25, 'elapsed_lm-eval-345m': 382}AssistantStep 5edit
Tool Input
1{
2 "end_line": 223,
3 "start_line": 188,
4 "content": "def compute_loss(logits, targets):\n \"\"\"\n Simpler Softcapping + z-loss without overcomplicating with Poly-1 loss.\n Motivation: \n - Softcap (A=30) alone provides strong regularization on max logit magnitude.\n - Z-loss prevents logit drift.\n \"\"\"\n flat_logits = logits.float().view(-1, logits.size(-1))\n flat_targets = targets.view(-1)\n \n A = 30.0\n capped = A * torch.tanh(flat_logits / A)\n \n if not torch.is_grad_enabled():\n return F.cross_entropy(capped, flat_targets, ignore_index=-1)\n \n valid_mask = flat_targets != -1\n valid_logits = capped[valid_mask]\n valid_targets = flat_targets[valid_mask]\n \n # Standard cross entropy with small label smoothing\n ce_loss = F.cross_entropy(valid_logits, valid_targets, label_smoothing=0.01)\n \n # Z-Loss Regularization\n log_z = torch.logsumexp(valid_logits, dim=-1)\n z_loss = (log_z ** 2).mean()\n \n return ce_loss + 1e-4 * z_loss",
5 "filename": "nanoGPT/custom_pretrain.py",
6 "op": "replace"
7}Tool ResultStep 5
OK: Replaced lines 188..223 with 28 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–215. [Current file: nanoGPT/custom_pretrain.py | editable: 188–215 | total: 463 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """ 190 Simpler Softcapping + z-loss without overcomplicating with Poly-1 loss. 191 Motivation: 192 - Softcap (A=30) alone provides strong regularization on max logit magnitude. 193 - Z-loss prevents logit drift. 194 """ 195 flat_logits = logits.float().view(-1, logits.size(-1)) 196 flat_targets = targets.view(-1) 197 198 A = 30.0 199 capped = A * torch.tanh(flat_logits / A) 200 201 if not torch.is_grad_enabled(): 202 return F.cross_entropy(capped, flat_targets, ignore_index=-1) 203 204 valid_mask
AssistantStep 6test
AssistantStep 6test
Tool ResultStep 6
[Test #3] (0 tests remaining; call submit(n=N) to choose which test result to submit as final)
[NOTE] This was your last test. You MUST now call submit(n=X) to choose which test result to submit as your final answer.
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=9000, train_loss=2.6427, val_loss=2.3672
TRAIN_METRICS: step=10000, train_loss=2.6208, val_loss=2.3388
TRAIN_METRICS: step=11000, train_loss=2.6022, val_loss=2.3292
TRAIN_METRICS: step=12000, train_loss=2.5829, val_loss=2.3113
TRAIN_METRICS: step=13000, train_loss=2.5671, val_loss=2.3007
Final metrics (gpt-345m): val_loss_gpt-345m=2.2948, wikitext2_ppl_gpt-345m=45.1200, lambada_ppl_gpt-345m=68.4300
### lm-eval-345m (scripts/lm_eval_345m.sh)
ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-loss/gemini-3.1-pro-preview_20260408_123026/seed_42/ckpt_gpt-345m.pt
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2948, 'wikitext2_ppl_gpt-345m': 45.12, 'l
AssistantStep 7submit
Tool Input
1{
2 "n": 3
3}Tool ResultStep 7
[submit] Submitting result from test #3 as final.
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=9000, train_loss=2.6427, val_loss=2.3672
TRAIN_METRICS: step=10000, train_loss=2.6208, val_loss=2.3388
TRAIN_METRICS: step=11000, train_loss=2.6022, val_loss=2.3292
TRAIN_METRICS: step=12000, train_loss=2.5829, val_loss=2.3113
TRAIN_METRICS: step=13000, train_loss=2.5671, val_loss=2.3007
Final metrics (gpt-345m): val_loss_gpt-345m=2.2948, wikitext2_ppl_gpt-345m=45.1200, lambada_ppl_gpt-345m=68.4300
### lm-eval-345m (scripts/lm_eval_345m.sh)
ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-loss/gemini-3.1-pro-preview_20260408_123026/seed_42/ckpt_gpt-345m.pt
[submit] Finalized test #3 as final.
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2948, 'wikitext2_ppl_gpt-345m': 45.12, 'lambada_ppl_gpt-345m': 68.43, 'elapsed_gpt-345m': 22975, 'elapsed_lm-eval-345m': 1}